Nonbinary and binary values in input tensor
If the two inputs for the binary values are held to the domain set $\{0, 1\}$ during training, testing, and use, it will not break the network functionality, although the question is a good one. Why use so many bits to hold one?

Theory of Holors: A Generalization of Tensors, by Parry Moon and Domina Spencer (ISBNs 978-0521019002 and 0521019001), propose holors precisely because of this kind of limitation on the natural heterogeneity of numerical structure. Gibbs, Ricci, Einstein, and others used holors in their mathematical expressions but without the name holor. Holors are are conceptually related to objects in ontology and classes in object oriented design, but they are numbers, so they fit into mathematical expressions as scalars, vectors, matrices, vector fields, and other tensors do.

This is the basis for Coplien's operator overload in C++ and one of the reasons the language is too mutable to be mastered by average applications programmers. Support for what Moon and Spenser called holors is also the basis of decoupled type safety in some of the early LISP object oriented frameworks.

The current programming paradigm we seen in Java, Python, and other popular languages was set in FORTRAN, which was more attainable for average programmers, so we are stuck with homogeneous structure, even in AI libraries and GPUs. Therefore, once one number requires an IEEE 64 float, bytes and bits will use all 64.

In artificial networks, this only matters in the very first multiplication with a parameter.